{"title":"A Comparative Study of Metabolic Network Topology between a Pathogenic and a Non-Pathogenic Bacterium for Potential Drug Target Identification.","authors":"Deepak Perumal, Chu Sing Lim, Meena K Sakharkar","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Metabolic network provides a unified platform to integrate all the biological information on genes, proteins, metabolites, drugs and drug targets for a comprehensive system level study of the relationship between metabolism and disease. In recent times, drug-target identification by in silico methods has emerged causing a phenomenal achievement in the field of drug discovery. This paper focuses on describing how microbial drug target identification can be carried out using bioinformatic tools. Specifically, it highlights the use of metabolic 'choke point' and 'load point' analyses to understand the local and global properties of metabolic networks in Pseudomonas aeruginosa and allow us to identify potential drug targets. We also list out top 10 choke point enzymes based on the load point values and the number of shortest paths. A non-pathogenic bacterial strain Pseudomonas putida KT2440 and a related pathogenic bacteria P.aeruginosa PA01 was selected for the network anlaysis. A comparative study of the metabolic networks of these two microbes highlights the analogies and differences between their respective pathways. System analysis of metabolic networks will help us in identifying new drug targets which in turn will generate more in-depth understanding of the mechanism of diseases and thus provide better guidance for drug discovery.</p>","PeriodicalId":89276,"journal":{"name":"Summit on translational bioinformatics","volume":"2009 ","pages":"100-4"},"PeriodicalIF":0.0,"publicationDate":"2009-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041556/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29694505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Automatically classifying sentences in full-text biomedical articles into introduction, methods, results and discussion.","authors":"Shashank Agarwal, Hong Yu","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>BIOMEDICAL TEXTS CAN BE TYPICALLY REPRESENTED BY FOUR RHETORICAL CATEGORIES: introduction, methods, results and discussion (IMRAD). Classifying sentences into these categories can benefit many other text-mining tasks. Although many studies have applied approaches to automatically classify sentences in MEDLINE abstracts into the IMRAD categories, few have explored the classification of sentences that appear in full-text biomedical articles. We explored different approaches to automatically classify a sentence in a full-text biomedical article into the IMRAD categories. Our best system is a support vector machine classifier that achieved 81.30% accuracy, which is significantly higher than baseline systems.</p>","PeriodicalId":89276,"journal":{"name":"Summit on translational bioinformatics","volume":"2009 ","pages":"6-10"},"PeriodicalIF":0.0,"publicationDate":"2009-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041564/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29694637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Richard H Scheuermann, Werner Ceusters, Barry Smith
{"title":"Toward an ontological treatment of disease and diagnosis.","authors":"Richard H Scheuermann, Werner Ceusters, Barry Smith","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Many existing biomedical vocabulary standards rest on incomplete, inconsistent or confused accounts of basic terms pertaining to diseases, diagnoses, and clinical phenotypes. Here we outline what we believe to be a logically and biologically coherent framework for the representation of such entities and of the relations between them. We defend a view of disease as involving in every case some physical basis within the organism that bears a disposition toward the execution of pathological processes. We present our view in the form of a list of terms and definitions designed to provide a consistent starting point for the representation of both disease and diagnosis in information systems in the future.</p>","PeriodicalId":89276,"journal":{"name":"Summit on translational bioinformatics","volume":"2009 ","pages":"116-20"},"PeriodicalIF":0.0,"publicationDate":"2009-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041577/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29693828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Translational systems genomics: ontology and imaging.","authors":"Su-Shing Chen, Yu-Ping Wang","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>In developing an integrated framework for translational bioinformatics, we consider bioimaging in the NIH Roadmap that exploits high-resolution genomic imaging for clinical applications to the diagnosis and treatment of genetic disorders/diseases. On one hand, we develop new image processing techniques, while on the other, we use the fusion of several well known ontological standards - Gene Ontology (GO), Clinical Bioinformatics Ontology (CBO), Foundational Model of Anatomy (FMA) and Microarry Gene Expression Data Ontology (MGED) in this framework. We have discovered that the heterogeneity of the imaging data can be resolved at the different ontological levels of this framework. Moreover, structural genomic information can be readily integrated into the usual textual clinical information bases.</p>","PeriodicalId":89276,"journal":{"name":"Summit on translational bioinformatics","volume":"2009 ","pages":"21-5"},"PeriodicalIF":0.0,"publicationDate":"2009-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041582/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29694054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Modeling and characterization of disease associated subnetworks in the human interactome using machine learning.","authors":"Lee T Sam, George Michailidis","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The availability of large-scale, genome-wide data about the molecular interactome of entire organisms has made possible new types of integrative studies, making use of rapidly accumulating knowledge of gene-disease associations. Previous studies have established the presence of functional biomodules in the molecular interaction network of living organisms, a number of which have been associated with the pathogenesis and progression of human disease. While a number of studies have examined the networks and biomodules associated with disease, the properties that contribute to the particular susceptibility of these subnetworks to disruptions leading to disease phenotypes have not been extensively studied. We take a machine learning approach to the characterization of these disease subnetworks associated with complex and single-gene diseases, taking into account both the biological roles of their constituent genes and topological properties of the networks they form.</p>","PeriodicalId":89276,"journal":{"name":"Summit on translational bioinformatics","volume":"2009 ","pages":"1-28"},"PeriodicalIF":0.0,"publicationDate":"2009-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041579/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29694630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Pattern discovery in breast cancer specific protein interaction network.","authors":"Xiaogang Wu, Scott H Harrison, Jake Yue Chen","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The interest in indentifying novel biomarkers for early stage breast cancer (BRCA) detection has become grown significantly in recent years. From a view of network biology, one of the emerging themes today is to re-characterize a protein's biological functions in its molecular network. Although many methods have been presented, including network-based gene ranking for molecular biomarker discovery, and graph clustering for functional module discovery, it is still hard to find systems-level properties hidden in disease specific molecular networks. We reconstructed BRCA-related protein interaction network by using BRCA-associated genes/proteins as seeds, and expanding them in an integrated protein interaction database. We further developed a computational framework based on Ant Colony Optimization to rank network nodes. The task of ranking nodes is represented as the problem of finding optimal density distributions of \"ant colonies\" on all nodes of the network. Our results revealed some interesting systems-level pattern in BRCA-related protein interaction network.</p>","PeriodicalId":89276,"journal":{"name":"Summit on translational bioinformatics","volume":"2009 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2009-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041566/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29694636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alberto Malovini, Carla Rognoni, Annibale Puca, Riccardo Bellazzi
{"title":"Multi-criteria decision making approaches for quality control of genome-wide association studies.","authors":"Alberto Malovini, Carla Rognoni, Annibale Puca, Riccardo Bellazzi","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Experimental errors in the genotyping phases of a Genome-Wide Association Study (GWAS) can lead to false positive findings and to spurious associations. An appropriate quality control phase could minimize the effects of this kind of errors. Several filtering criteria can be used to perform quality control. Currently, no formal methods have been proposed for taking into account at the same time these criteria and the experimenter's preferences. In this paper we propose two strategies for setting appropriate genotyping rate thresholds for GWAS quality control. These two approaches are based on the Multi-Criteria Decision Making theory. We have applied our method on a real dataset composed by 734 individuals affected by Arterial Hypertension (AH) and 486 nonagenarians without history of AH. The proposed strategies appear to deal with GWAS quality control in a sound way, as they lead to rationalize and make explicit the experimenter's choices thus providing more reproducible results.</p>","PeriodicalId":89276,"journal":{"name":"Summit on translational bioinformatics","volume":"2009 ","pages":"74-8"},"PeriodicalIF":0.0,"publicationDate":"2009-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041572/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29694500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Using the weighted keyword model to improve information retrieval for answering biomedical questions.","authors":"Hong Yu, Yong-Gang Cao","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Physicians ask many complex questions during the patient encounter. Information retrieval systems that can provide immediate and relevant answers to these questions can be invaluable aids to the practice of evidence-based medicine. In this study, we first automatically identify topic keywords from ad hoc clinical questions with a Condition Random Field model that is trained over thousands of manually annotated clinical questions. We then report on a linear model that assigns query weights based on their automatically identified semantic roles: topic keywords, domain specific terms, and their synonyms. Our evaluation shows that this weighted keyword model improves information retrieval from the Text Retrieval Conference Genomics track data.</p>","PeriodicalId":89276,"journal":{"name":"Summit on translational bioinformatics","volume":"2009 ","pages":"143-7"},"PeriodicalIF":0.0,"publicationDate":"2009-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041568/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29693834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Extraction of Conditional Probabilities of the Relationships Between Drugs, Diseases, and Genes from PubMed Guided by Relationships in PharmGKB.","authors":"Martin Theobald, Nigam Shah, Jeff Shrager","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Guided by curated associations between genes, treatments (i.e., drugs), and diseases in pharmGKB, we constructed n-way Bayesian networks based on conditional probability tables (cpt's) extracted from co-occurrence statistics over the entire Pubmed corpus, producing a broad-coverage analysis of the relationships between these biological entities. The networks suggest hypotheses regarding drug mechanisms, treatment biomarkers, and/or potential markers of genetic disease. The cpt's enable Trio, an inferential database, to query indirect (inferred) relationships via an SQL-like query language.</p>","PeriodicalId":89276,"journal":{"name":"Summit on translational bioinformatics","volume":"2009 ","pages":"124-8"},"PeriodicalIF":0.0,"publicationDate":"2009-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041559/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29693829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Bayesian combinatorial partitioning for detecting interactions among genetic variants.","authors":"Shyam Visweswaran, An-Kwok Ian Wong","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Detecting epistatic (nolinear) interactions among single nucleotide polymorphisms (SNPs) at multiple loci is important in the analysis of genomic data in association studies. We developed a Bayesian combinatorial partitioning (BCP) for detecting such interactions among SNPs that are predictive of disease. When compared with multifactor dimensionality reduction (MDR), a widely used combinatorial partitioning method for detecting interactions, BCP has significantly greater power and is computationally more efficient.</p>","PeriodicalId":89276,"journal":{"name":"Summit on translational bioinformatics","volume":"2009 ","pages":"133"},"PeriodicalIF":0.0,"publicationDate":"2009-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041553/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29693831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}